Genome-scale metabolic network reconstruction analysis identifies bacterial vaginosis-associated metabolic interactions
Abstract Bacterial vaginosis (BV) is the most prevalent vaginal condition among reproductive-age women presenting with vaginal complaints. Despite its significant impact on women’s health, limited knowledge exists regarding the microbial community composition and metabolic interactions associated wi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59965-y |
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| author | Lillian R. Dillard Emma M. Glass Glynis L. Kolling Krystal Thomas-White Fiorella Wever Robert Markowitz David Lyttle Jason A. Papin |
| author_facet | Lillian R. Dillard Emma M. Glass Glynis L. Kolling Krystal Thomas-White Fiorella Wever Robert Markowitz David Lyttle Jason A. Papin |
| author_sort | Lillian R. Dillard |
| collection | DOAJ |
| description | Abstract Bacterial vaginosis (BV) is the most prevalent vaginal condition among reproductive-age women presenting with vaginal complaints. Despite its significant impact on women’s health, limited knowledge exists regarding the microbial community composition and metabolic interactions associated with BV. In this study, we analyze metagenomic data obtained from human vaginal swabs to generate in silico predictions of BV-associated bacterial metabolic interactions via genome-scale metabolic network reconstructions (GENREs). While most efforts to characterize symptomatic BV (and thus guide therapeutic intervention by identifying responders and non-responders to treatment) are based on genomic profiling, our in silico simulations reveal functional metabolic relatedness between species as quite distinct from genetic relatedness. We grow several of the most common co-occurring bacteria (Prevotella amnii, Prevotella buccalis, Hoylesella timonensis, Lactobacillus iners, Fannyhessea vaginae, and Aerrococcus christenssii) on the spent media of Gardnerella species and perform metabolomics to identify potential mechanisms of metabolic interaction. Through these analyses, we identify BV-associated bacteria that produce caffeate, a compound implicated in estrogen receptor binding, when grown in the spent media of other BV-associated bacteria. These findings underscore the complex and diverse nature of BV-associated bacterial community structures and several of these mechanisms are of potential significance in understanding host-microbiome relationships. |
| format | Article |
| id | doaj-art-d1b4be75469f47c69a8ebb25ecebf7f0 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-d1b4be75469f47c69a8ebb25ecebf7f02025-08-20T03:48:18ZengNature PortfolioNature Communications2041-17232025-05-0116111110.1038/s41467-025-59965-yGenome-scale metabolic network reconstruction analysis identifies bacterial vaginosis-associated metabolic interactionsLillian R. Dillard0Emma M. Glass1Glynis L. Kolling2Krystal Thomas-White3Fiorella Wever4Robert Markowitz5David Lyttle6Jason A. Papin7Department of Biochemistry & Molecular Genetics, University of VirginiaDepartment of Biomedical Engineering, University of VirginiaDepartment of Biomedical Engineering, University of VirginiaEvvyEvvyEvvyEvvyDepartment of Biochemistry & Molecular Genetics, University of VirginiaAbstract Bacterial vaginosis (BV) is the most prevalent vaginal condition among reproductive-age women presenting with vaginal complaints. Despite its significant impact on women’s health, limited knowledge exists regarding the microbial community composition and metabolic interactions associated with BV. In this study, we analyze metagenomic data obtained from human vaginal swabs to generate in silico predictions of BV-associated bacterial metabolic interactions via genome-scale metabolic network reconstructions (GENREs). While most efforts to characterize symptomatic BV (and thus guide therapeutic intervention by identifying responders and non-responders to treatment) are based on genomic profiling, our in silico simulations reveal functional metabolic relatedness between species as quite distinct from genetic relatedness. We grow several of the most common co-occurring bacteria (Prevotella amnii, Prevotella buccalis, Hoylesella timonensis, Lactobacillus iners, Fannyhessea vaginae, and Aerrococcus christenssii) on the spent media of Gardnerella species and perform metabolomics to identify potential mechanisms of metabolic interaction. Through these analyses, we identify BV-associated bacteria that produce caffeate, a compound implicated in estrogen receptor binding, when grown in the spent media of other BV-associated bacteria. These findings underscore the complex and diverse nature of BV-associated bacterial community structures and several of these mechanisms are of potential significance in understanding host-microbiome relationships.https://doi.org/10.1038/s41467-025-59965-y |
| spellingShingle | Lillian R. Dillard Emma M. Glass Glynis L. Kolling Krystal Thomas-White Fiorella Wever Robert Markowitz David Lyttle Jason A. Papin Genome-scale metabolic network reconstruction analysis identifies bacterial vaginosis-associated metabolic interactions Nature Communications |
| title | Genome-scale metabolic network reconstruction analysis identifies bacterial vaginosis-associated metabolic interactions |
| title_full | Genome-scale metabolic network reconstruction analysis identifies bacterial vaginosis-associated metabolic interactions |
| title_fullStr | Genome-scale metabolic network reconstruction analysis identifies bacterial vaginosis-associated metabolic interactions |
| title_full_unstemmed | Genome-scale metabolic network reconstruction analysis identifies bacterial vaginosis-associated metabolic interactions |
| title_short | Genome-scale metabolic network reconstruction analysis identifies bacterial vaginosis-associated metabolic interactions |
| title_sort | genome scale metabolic network reconstruction analysis identifies bacterial vaginosis associated metabolic interactions |
| url | https://doi.org/10.1038/s41467-025-59965-y |
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